Disaster Sensing and Revocation using NC Cloud during emergency

Abstract: The main objective of this project is to enhance the data storage security during disaster. So that IaaS (Infrastructure as a Service) methodology will be implemented here. This is to provide more security for the storage devices during malware attacks and also during disaster. The remote monitoring system is growing very rapidly due to the growth of supporting technologies as well. And also problem that may occur in remote monitoring such as the number of objects to be monitored and how fast, how much data to be transmitted to the data centre to be processed properly. This study proposes using a cloud computing infrastructure as processing centre in the remote sensing data. This study focuses on the situation for sensing on the environment condition and disaster early detection. Where those two things, it has become an important issue, especially in big cities big cities that have many residents. This study proposes to build the conceptual and also prototype model in a comprehensive manner from the remote terminal unit until development method for data retrieval. We also propose using DCN method to guarantee the delivery from remote client to server. In added with the remote monitoring system will keep on tracking the database architecture for the data transfer. Whenever destruction occur the data base architecture will transfer the database to the concern location assigned from the admin. So that data base can be saving exactly with the last fine transaction. Here data loss will not occur at any cost. This method is based on IP conflict procedure. So that roll backing process can also be possible. Using the same procedure of IP conflict method and this method will shows the data up to last minute transaction. Enhanced DCN has been used for encrypt the data during the time of data transaction. When a database has been encrypted using a DCN means, the DB could not access by third party.

Keywords: Disaster analysis, local optimization, remote monitoring, software engineering, Data processing